Air-breathing hypersonic vehicle trajectory optimization with uncertain no-fly zones

Abstract

The air-breathing hypersonic planes are considered to be the future of commercial airlines, which can fly from NYC to London in under an hour. For air-breathing vehicles, 3D trajectory planning will become extremely important due to its significant impact on flight performance. Past research on this issue was not comprehensive enough. Thus, we proposed a hybrid method for generating the optimal trajectories efficiently. No-fly zones are specified for geopolitical restrictions in air-breathing hypersonic vehicle missions. However, previous studies focused on no-fly zone constraints with fixed locations and boundaries. For robust execution, we must take into account no-fly zones’ uncertainties, which arise due to uncertain localization, measurement errors, and no-fly zones’ movement. A chance-constrained approach is presented here to deal with such uncertainties. The key idea of this method is controlling risk when the flight path is close to the uncertain no-fly zones. First, the trajectory planning of air-breathing hypersonic vehicles is modeled as a chance-constrained optimization (CCOPT) problem. With the help of the convexification and linearization techniques, we can approximate the non 3.2.2 CCOPT problem as a convex second-order cone programing under the Gaussian distribution assumption. It can be solved to global optimality by the interior point method. Finally, we give numerical simulation results to prove the effectiveness of this method.

Preface to the special issue on numerical simulations and optimization under uncertainty

“…Three articles in the discipline of modeling under uncertainty and indeterminacy have been chosen: Tian and Shen 1 initially presented a hybrid optimization method based on the flight characteristics of air-breathing hypersonic vehicle (ABHV). The numerical demonstrations demonstrate that this strategy can successfully handle the ABHV trajectory optimization problem.…”

Intelligent Maneuver Strategy for a Hypersonic Pursuit-Evasion Game Based on Deep Reinforcement Learning

“…In the past, hypersonic aircraft mainly used traditional solutions, including unilateral trajectory planning [5][6][7][8][9][10][11] and bilateral game maneuvering [12][13][14][15][16][17][18][19], to deal with the PE problem.…”

“…Unilateral trajectory planning is achieved by pre-planning a trajectory and optimizing it to bypass the interceptor using optimal control [5][6][7][8] or other algorithms [9][10][11]. In the trajectory optimization strategy mentioned above, the literature [5] considers the optimization of hypersonic glide vehicle (HGV) evasion trajectory as a nonconvex optimal control problem and solves the second-order cone programming (SOCP) problem by state-of-theart interior-point methods.…”